Overview

Dataset statistics

Number of variables48
Number of observations20000
Missing cells155531
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory378.0 B

Variable types

Unsupported16
Text9
Categorical9
Numeric8
DateTime4
Boolean2

Alerts

site_id has constant value ""Constant
listing_source has constant value ""Constant
international_delivery_mode has constant value ""Constant
base_price is highly overall correlated with price and 2 other fieldsHigh correlation
seller_id is highly overall correlated with catalog_product_idHigh correlation
price is highly overall correlated with base_price and 2 other fieldsHigh correlation
official_store_id is highly overall correlated with buying_mode and 3 other fieldsHigh correlation
original_price is highly overall correlated with base_price and 6 other fieldsHigh correlation
initial_quantity is highly overall correlated with available_quantity and 1 other fieldsHigh correlation
sold_quantity is highly overall correlated with original_price and 1 other fieldsHigh correlation
available_quantity is highly overall correlated with initial_quantity and 1 other fieldsHigh correlation
condition is highly overall correlated with original_price and 2 other fieldsHigh correlation
listing_type_id is highly overall correlated with condition and 2 other fieldsHigh correlation
buying_mode is highly overall correlated with official_store_id and 4 other fieldsHigh correlation
accepts_mercadopago is highly overall correlated with official_store_id and 4 other fieldsHigh correlation
currency_id is highly overall correlated with official_store_id and 4 other fieldsHigh correlation
automatic_relist is highly overall correlated with official_store_id and 2 other fieldsHigh correlation
status is highly overall correlated with catalog_product_idHigh correlation
catalog_product_id is highly overall correlated with base_price and 12 other fieldsHigh correlation
buying_mode is highly imbalanced (85.8%)Imbalance
accepts_mercadopago is highly imbalanced (84.3%)Imbalance
currency_id is highly imbalanced (94.8%)Imbalance
automatic_relist is highly imbalanced (73.2%)Imbalance
status is highly imbalanced (83.6%)Imbalance
warranty has 12194 (61.0%) missing valuesMissing
seller_contact has 19543 (97.7%) missing valuesMissing
parent_item_id has 4595 (23.0%) missing valuesMissing
official_store_id has 19824 (99.1%) missing valuesMissing
differential_pricing has 20000 (100.0%) missing valuesMissing
original_price has 19970 (99.9%) missing valuesMissing
video_id has 19407 (97.0%) missing valuesMissing
catalog_product_id has 19998 (> 99.9%) missing valuesMissing
subtitle has 20000 (100.0%) missing valuesMissing
base_price is highly skewed (γ1 = 141.4005313)Skewed
price is highly skewed (γ1 = 141.4005313)Skewed
initial_quantity is highly skewed (γ1 = 22.38778158)Skewed
sold_quantity is highly skewed (γ1 = 96.45057639)Skewed
available_quantity is highly skewed (γ1 = 22.40908514)Skewed
catalog_product_id is uniformly distributedUniform
id has unique valuesUnique
permalink has unique valuesUnique
seller_address is an unsupported type, check if it needs cleaning or further analysisUnsupported
sub_status is an unsupported type, check if it needs cleaning or further analysisUnsupported
seller_contact is an unsupported type, check if it needs cleaning or further analysisUnsupported
deal_ids is an unsupported type, check if it needs cleaning or further analysisUnsupported
shipping is an unsupported type, check if it needs cleaning or further analysisUnsupported
non_mercado_pago_payment_methods is an unsupported type, check if it needs cleaning or further analysisUnsupported
variations is an unsupported type, check if it needs cleaning or further analysisUnsupported
location is an unsupported type, check if it needs cleaning or further analysisUnsupported
attributes is an unsupported type, check if it needs cleaning or further analysisUnsupported
tags is an unsupported type, check if it needs cleaning or further analysisUnsupported
coverage_areas is an unsupported type, check if it needs cleaning or further analysisUnsupported
descriptions is an unsupported type, check if it needs cleaning or further analysisUnsupported
pictures is an unsupported type, check if it needs cleaning or further analysisUnsupported
differential_pricing is an unsupported type, check if it needs cleaning or further analysisUnsupported
subtitle is an unsupported type, check if it needs cleaning or further analysisUnsupported
geolocation is an unsupported type, check if it needs cleaning or further analysisUnsupported
sold_quantity has 16618 (83.1%) zerosZeros

Reproduction

Analysis started2023-07-25 03:18:27.448895
Analysis finished2023-07-25 03:27:08.298229
Duration8 minutes and 40.85 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

seller_address
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

warranty
Text

MISSING 

Distinct3050
Distinct (%)39.1%
Missing12194
Missing (%)61.0%
Memory size312.5 KiB
2023-07-24T21:27:08.499456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length260
Median length241
Mean length39.554445
Min length1

Characters and Unicode

Total characters308762
Distinct characters112
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2401 ?
Unique (%)30.8%

Sample

1st rowCon garantia.
2nd row
3rd rowSin garantía
4th rowLA REPUTACION DEL HERMANO MANSON
5th rowDistribuidores oficiales
ValueCountFrequency (%)
de 3685
 
7.2%
garantía 1900
 
3.7%
sin 1255
 
2.4%
el 1254
 
2.4%
1195
 
2.3%
por 1192
 
2.3%
en 1179
 
2.3%
la 1148
 
2.2%
garantia 1027
 
2.0%
y 1006
 
2.0%
Other values (3364) 36563
71.1%
2023-07-24T21:27:08.833256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43952
 
14.2%
a 23579
 
7.6%
e 19591
 
6.3%
o 14922
 
4.8%
n 13251
 
4.3%
r 12912
 
4.2%
i 12525
 
4.1%
s 11743
 
3.8%
t 9783
 
3.2%
A 9440
 
3.1%
Other values (102) 137064
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173113
56.1%
Uppercase Letter 80642
26.1%
Space Separator 43952
 
14.2%
Other Punctuation 6182
 
2.0%
Decimal Number 3803
 
1.2%
Control 364
 
0.1%
Dash Punctuation 330
 
0.1%
Close Punctuation 143
 
< 0.1%
Open Punctuation 139
 
< 0.1%
Math Symbol 24
 
< 0.1%
Other values (6) 70
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23579
13.6%
e 19591
11.3%
o 14922
8.6%
n 13251
 
7.7%
r 12912
 
7.5%
i 12525
 
7.2%
s 11743
 
6.8%
t 9783
 
5.7%
c 9326
 
5.4%
d 8405
 
4.9%
Other values (26) 37076
21.4%
Uppercase Letter
ValueCountFrequency (%)
A 9440
11.7%
S 8663
10.7%
E 8562
10.6%
O 7320
9.1%
I 5713
 
7.1%
R 5359
 
6.6%
N 4863
 
6.0%
T 4566
 
5.7%
C 4479
 
5.6%
L 3945
 
4.9%
Other values (24) 17732
22.0%
Other Punctuation
ValueCountFrequency (%)
. 3364
54.4%
, 1276
 
20.6%
! 804
 
13.0%
% 275
 
4.4%
/ 150
 
2.4%
: 83
 
1.3%
¡ 79
 
1.3%
* 74
 
1.2%
" 34
 
0.5%
; 18
 
0.3%
Other values (4) 25
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 1029
27.1%
1 930
24.5%
6 642
16.9%
3 384
 
10.1%
2 331
 
8.7%
5 160
 
4.2%
4 92
 
2.4%
7 89
 
2.3%
9 78
 
2.1%
8 68
 
1.8%
Control
ValueCountFrequency (%)
182
50.0%
182
50.0%
Close Punctuation
ValueCountFrequency (%)
) 142
99.3%
] 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 138
99.3%
[ 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 19
79.2%
= 5
 
20.8%
Other Symbol
ValueCountFrequency (%)
® 9
75.0%
° 3
 
25.0%
Other Letter
ValueCountFrequency (%)
º 9
81.8%
ª 2
 
18.2%
Space Separator
ValueCountFrequency (%)
43952
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 330
100.0%
Other Number
ValueCountFrequency (%)
³ 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 253766
82.2%
Common 54996
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23579
 
9.3%
e 19591
 
7.7%
o 14922
 
5.9%
n 13251
 
5.2%
r 12912
 
5.1%
i 12525
 
4.9%
s 11743
 
4.6%
t 9783
 
3.9%
A 9440
 
3.7%
c 9326
 
3.7%
Other values (62) 116694
46.0%
Common
ValueCountFrequency (%)
43952
79.9%
. 3364
 
6.1%
, 1276
 
2.3%
0 1029
 
1.9%
1 930
 
1.7%
! 804
 
1.5%
6 642
 
1.2%
3 384
 
0.7%
2 331
 
0.6%
- 330
 
0.6%
Other values (30) 1954
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302944
98.1%
None 5817
 
1.9%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43952
 
14.5%
a 23579
 
7.8%
e 19591
 
6.5%
o 14922
 
4.9%
n 13251
 
4.4%
r 12912
 
4.3%
i 12525
 
4.1%
s 11743
 
3.9%
t 9783
 
3.2%
A 9440
 
3.1%
Other values (75) 131246
43.3%
None
ValueCountFrequency (%)
í 3351
57.6%
ó 811
 
13.9%
ñ 433
 
7.4%
á 274
 
4.7%
Ñ 215
 
3.7%
Í 181
 
3.1%
Ó 143
 
2.5%
¡ 79
 
1.4%
é 74
 
1.3%
Á 62
 
1.1%
Other values (16) 194
 
3.3%
Punctuation
ValueCountFrequency (%)
1
100.0%

sub_status
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

condition
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
new
10842 
used
9158 

Length

Max length4
Median length3
Mean length3.4579
Min length3

Characters and Unicode

Total characters69158
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownew
2nd rownew
3rd rownew
4th rowused
5th rowused

Common Values

ValueCountFrequency (%)
new 10842
54.2%
used 9158
45.8%

Length

2023-07-24T21:27:08.937385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:09.032803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
new 10842
54.2%
used 9158
45.8%

Most occurring characters

ValueCountFrequency (%)
e 20000
28.9%
n 10842
15.7%
w 10842
15.7%
u 9158
13.2%
s 9158
13.2%
d 9158
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 69158
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20000
28.9%
n 10842
15.7%
w 10842
15.7%
u 9158
13.2%
s 9158
13.2%
d 9158
13.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 69158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20000
28.9%
n 10842
15.7%
w 10842
15.7%
u 9158
13.2%
s 9158
13.2%
d 9158
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20000
28.9%
n 10842
15.7%
w 10842
15.7%
u 9158
13.2%
s 9158
13.2%
d 9158
13.2%

seller_contact
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19543
Missing (%)97.7%
Memory size312.5 KiB

deal_ids
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

base_price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3494
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61125.894
Minimum1
Maximum1.1111111 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:09.125803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q195
median250
Q3800
95-th percentile7488.6
Maximum1.1111111 × 109
Range1.1111111 × 109
Interquartile range (IQR)705

Descriptive statistics

Standard deviation7857089.7
Coefficient of variation (CV)128.53947
Kurtosis19996.058
Mean61125.894
Median Absolute Deviation (MAD)196.08
Skewness141.40053
Sum1.2225179 × 109
Variance6.1733859 × 1013
MonotonicityNot monotonic
2023-07-24T21:27:09.233804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 610
 
3.0%
100 591
 
3.0%
150 472
 
2.4%
60 431
 
2.2%
200 390
 
1.9%
40 362
 
1.8%
120 348
 
1.7%
80 341
 
1.7%
250 325
 
1.6%
70 313
 
1.6%
Other values (3484) 15817
79.1%
ValueCountFrequency (%)
1 15
0.1%
1.5 1
 
< 0.1%
2 2
 
< 0.1%
2.25 1
 
< 0.1%
2.3 1
 
< 0.1%
2.98 1
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
4.5 2
 
< 0.1%
5 13
0.1%
ValueCountFrequency (%)
1111111111 1
< 0.1%
9000000 1
< 0.1%
2300000 1
< 0.1%
1700000 1
< 0.1%
1500000 1
< 0.1%
1499000 1
< 0.1%
1460000 1
< 0.1%
1400000 1
< 0.1%
1350000 1
< 0.1%
1200000 1
< 0.1%

shipping
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

non_mercado_pago_payment_methods
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

seller_id
Real number (ℝ)

HIGH CORRELATION 

Distinct11088
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84444920
Minimum5857
Maximum1.9469058 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:09.345799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5857
5-th percentile7125093
Q140202816
median76394902
Q31.3260448 × 108
95-th percentile1.8190116 × 108
Maximum1.9469058 × 108
Range1.9468472 × 108
Interquartile range (IQR)92401662

Descriptive statistics

Standard deviation55078888
Coefficient of variation (CV)0.65224632
Kurtosis-1.0139511
Mean84444920
Median Absolute Deviation (MAD)42274160
Skewness0.37070393
Sum1.6888984 × 1012
Variance3.0336839 × 1015
MonotonicityNot monotonic
2023-07-24T21:27:09.458969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52340590 183
 
0.9%
35235505 157
 
0.8%
76310627 90
 
0.4%
7125093 82
 
0.4%
31210885 75
 
0.4%
130511705 68
 
0.3%
58869788 66
 
0.3%
147629117 50
 
0.2%
164929499 50
 
0.2%
7936636 49
 
0.2%
Other values (11078) 19130
95.7%
ValueCountFrequency (%)
5857 1
 
< 0.1%
39092 9
< 0.1%
96560 2
 
< 0.1%
101573 8
< 0.1%
102333 1
 
< 0.1%
102496 2
 
< 0.1%
102671 2
 
< 0.1%
102978 1
 
< 0.1%
102997 2
 
< 0.1%
103579 1
 
< 0.1%
ValueCountFrequency (%)
194690579 1
< 0.1%
194683882 1
< 0.1%
194601697 1
< 0.1%
194592971 1
< 0.1%
194587222 1
< 0.1%
194547226 1
< 0.1%
194539374 1
< 0.1%
194501387 1
< 0.1%
194497591 1
< 0.1%
194491708 1
< 0.1%

variations
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

location
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

site_id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
MLA
20000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMLA
2nd rowMLA
3rd rowMLA
4th rowMLA
5th rowMLA

Common Values

ValueCountFrequency (%)
MLA 20000
100.0%

Length

2023-07-24T21:27:09.560018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:09.637052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mla 20000
100.0%

Most occurring characters

ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

listing_type_id
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
bronze
12706 
free
4221 
silver
1802 
gold_special
 
610
gold
 
474
Other values (2)
 
187

Length

Max length12
Median length6
Mean length5.7688
Min length4

Characters and Unicode

Total characters115376
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgold
2nd rowbronze
3rd rowbronze
4th rowbronze
5th rowbronze

Common Values

ValueCountFrequency (%)
bronze 12706
63.5%
free 4221
 
21.1%
silver 1802
 
9.0%
gold_special 610
 
3.0%
gold 474
 
2.4%
gold_premium 183
 
0.9%
gold_pro 4
 
< 0.1%

Length

2023-07-24T21:27:09.715018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:09.819239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
bronze 12706
63.5%
free 4221
 
21.1%
silver 1802
 
9.0%
gold_special 610
 
3.0%
gold 474
 
2.4%
gold_premium 183
 
0.9%
gold_pro 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 23743
20.6%
r 18916
16.4%
o 13981
12.1%
b 12706
11.0%
n 12706
11.0%
z 12706
11.0%
f 4221
 
3.7%
l 3683
 
3.2%
i 2595
 
2.2%
s 2412
 
2.1%
Other values (9) 7707
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 114579
99.3%
Connector Punctuation 797
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23743
20.7%
r 18916
16.5%
o 13981
12.2%
b 12706
11.1%
n 12706
11.1%
z 12706
11.1%
f 4221
 
3.7%
l 3683
 
3.2%
i 2595
 
2.3%
s 2412
 
2.1%
Other values (8) 6910
 
6.0%
Connector Punctuation
ValueCountFrequency (%)
_ 797
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114579
99.3%
Common 797
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23743
20.7%
r 18916
16.5%
o 13981
12.2%
b 12706
11.1%
n 12706
11.1%
z 12706
11.1%
f 4221
 
3.7%
l 3683
 
3.2%
i 2595
 
2.3%
s 2412
 
2.1%
Other values (8) 6910
 
6.0%
Common
ValueCountFrequency (%)
_ 797
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23743
20.6%
r 18916
16.4%
o 13981
12.1%
b 12706
11.0%
n 12706
11.0%
z 12706
11.0%
f 4221
 
3.7%
l 3683
 
3.2%
i 2595
 
2.2%
s 2412
 
2.1%
Other values (9) 7707
 
6.7%

price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3495
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61125.934
Minimum1
Maximum1.1111111 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:09.933245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q195
median250
Q3800
95-th percentile7488.6
Maximum1.1111111 × 109
Range1.1111111 × 109
Interquartile range (IQR)705

Descriptive statistics

Standard deviation7857089.7
Coefficient of variation (CV)128.53938
Kurtosis19996.058
Mean61125.934
Median Absolute Deviation (MAD)196.08
Skewness141.40053
Sum1.2225187 × 109
Variance6.1733859 × 1013
MonotonicityNot monotonic
2023-07-24T21:27:10.043531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 610
 
3.0%
100 591
 
3.0%
150 472
 
2.4%
60 431
 
2.2%
200 390
 
1.9%
40 362
 
1.8%
120 348
 
1.7%
80 341
 
1.7%
250 325
 
1.6%
70 313
 
1.6%
Other values (3485) 15817
79.1%
ValueCountFrequency (%)
1 12
0.1%
1.5 1
 
< 0.1%
2 2
 
< 0.1%
2.25 1
 
< 0.1%
2.3 1
 
< 0.1%
2.98 1
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
4.5 2
 
< 0.1%
5 13
0.1%
ValueCountFrequency (%)
1111111111 1
< 0.1%
9000000 1
< 0.1%
2300000 1
< 0.1%
1700000 1
< 0.1%
1500000 1
< 0.1%
1499000 1
< 0.1%
1460000 1
< 0.1%
1400000 1
< 0.1%
1350000 1
< 0.1%
1200000 1
< 0.1%

attributes
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

buying_mode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
buy_it_now
19382 
classified
 
457
auction
 
161

Length

Max length10
Median length10
Mean length9.97585
Min length7

Characters and Unicode

Total characters199517
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbuy_it_now
2nd rowbuy_it_now
3rd rowbuy_it_now
4th rowbuy_it_now
5th rowbuy_it_now

Common Values

ValueCountFrequency (%)
buy_it_now 19382
96.9%
classified 457
 
2.3%
auction 161
 
0.8%

Length

2023-07-24T21:27:10.139575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:10.230638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
buy_it_now 19382
96.9%
classified 457
 
2.3%
auction 161
 
0.8%

Most occurring characters

ValueCountFrequency (%)
_ 38764
19.4%
i 20457
10.3%
u 19543
9.8%
t 19543
9.8%
n 19543
9.8%
o 19543
9.8%
b 19382
9.7%
y 19382
9.7%
w 19382
9.7%
s 914
 
0.5%
Other values (6) 3064
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160753
80.6%
Connector Punctuation 38764
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 20457
12.7%
u 19543
12.2%
t 19543
12.2%
n 19543
12.2%
o 19543
12.2%
b 19382
12.1%
y 19382
12.1%
w 19382
12.1%
s 914
 
0.6%
c 618
 
0.4%
Other values (5) 2446
 
1.5%
Connector Punctuation
ValueCountFrequency (%)
_ 38764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160753
80.6%
Common 38764
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 20457
12.7%
u 19543
12.2%
t 19543
12.2%
n 19543
12.2%
o 19543
12.2%
b 19382
12.1%
y 19382
12.1%
w 19382
12.1%
s 914
 
0.6%
c 618
 
0.4%
Other values (5) 2446
 
1.5%
Common
ValueCountFrequency (%)
_ 38764
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 38764
19.4%
i 20457
10.3%
u 19543
9.8%
t 19543
9.8%
n 19543
9.8%
o 19543
9.8%
b 19382
9.7%
y 19382
9.7%
w 19382
9.7%
s 914
 
0.5%
Other values (6) 3064
 
1.5%

tags
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

listing_source
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
20000 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
20000
100.0%

Length

2023-07-24T21:27:10.326513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:10.421667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

parent_item_id
Text

MISSING 

Distinct15405
Distinct (%)100.0%
Missing4595
Missing (%)23.0%
Memory size312.5 KiB
2023-07-24T21:27:10.558642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters184860
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15405 ?
Unique (%)100.0%

Sample

1st rowMLA566029044
2nd rowMLA564993666
3rd rowMLA568624909
4th rowMLA567996849
5th rowMLA572720444
ValueCountFrequency (%)
mla566029044 1
 
< 0.1%
mla583372022 1
 
< 0.1%
mla573857837 1
 
< 0.1%
mla573148236 1
 
< 0.1%
mla568624909 1
 
< 0.1%
mla567996849 1
 
< 0.1%
mla572720444 1
 
< 0.1%
mla570755559 1
 
< 0.1%
mla568578906 1
 
< 0.1%
mla574586704 1
 
< 0.1%
Other values (15395) 15395
99.9%
2023-07-24T21:27:10.816647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 26335
14.2%
6 18173
9.8%
7 17890
9.7%
M 15405
8.3%
L 15405
8.3%
A 15405
8.3%
2 11168
 
6.0%
8 11025
 
6.0%
9 10877
 
5.9%
0 10860
 
5.9%
Other values (3) 32317
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 138645
75.0%
Uppercase Letter 46215
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 26335
19.0%
6 18173
13.1%
7 17890
12.9%
2 11168
8.1%
8 11025
8.0%
9 10877
7.8%
0 10860
7.8%
1 10857
7.8%
3 10849
7.8%
4 10611
7.7%
Uppercase Letter
ValueCountFrequency (%)
M 15405
33.3%
L 15405
33.3%
A 15405
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 138645
75.0%
Latin 46215
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 26335
19.0%
6 18173
13.1%
7 17890
12.9%
2 11168
8.1%
8 11025
8.0%
9 10877
7.8%
0 10860
7.8%
1 10857
7.8%
3 10849
7.8%
4 10611
7.7%
Latin
ValueCountFrequency (%)
M 15405
33.3%
L 15405
33.3%
A 15405
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 26335
14.2%
6 18173
9.8%
7 17890
9.7%
M 15405
8.3%
L 15405
8.3%
A 15405
8.3%
2 11168
 
6.0%
8 11025
 
6.0%
9 10877
 
5.9%
0 10860
 
5.9%
Other values (3) 32317
17.5%

coverage_areas
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB
Distinct5500
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:10.998848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.8487
Min length7

Characters and Unicode

Total characters156974
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2836 ?
Unique (%)14.2%

Sample

1st rowMLA6446
2nd rowMLA3530
3rd rowMLA41287
4th rowMLA41174
5th rowMLA41068
ValueCountFrequency (%)
mla1227 906
 
4.5%
mla2044 390
 
1.9%
mla41287 196
 
1.0%
mla3530 175
 
0.9%
mla2038 151
 
0.8%
mla15171 107
 
0.5%
mla41269 89
 
0.4%
mla1383 89
 
0.4%
mla15204 87
 
0.4%
mla85960 83
 
0.4%
Other values (5490) 17727
88.6%
2023-07-24T21:27:11.272928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 20000
12.7%
L 20000
12.7%
A 20000
12.7%
1 14197
9.0%
2 12241
7.8%
3 11786
7.5%
4 10639
6.8%
7 9727
6.2%
0 8878
5.7%
5 8053
 
5.1%
Other values (3) 21453
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96974
61.8%
Uppercase Letter 60000
38.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14197
14.6%
2 12241
12.6%
3 11786
12.2%
4 10639
11.0%
7 9727
10.0%
0 8878
9.2%
5 8053
8.3%
6 7975
8.2%
9 7149
7.4%
8 6329
6.5%
Uppercase Letter
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 96974
61.8%
Latin 60000
38.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14197
14.6%
2 12241
12.6%
3 11786
12.2%
4 10639
11.0%
7 9727
10.0%
0 8878
9.2%
5 8053
8.3%
6 7975
8.2%
9 7149
7.4%
8 6329
6.5%
Latin
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 20000
12.7%
L 20000
12.7%
A 20000
12.7%
1 14197
9.0%
2 12241
7.8%
3 11786
7.5%
4 10639
6.8%
7 9727
6.2%
0 8878
5.7%
5 8053
 
5.1%
Other values (3) 21453
13.7%

descriptions
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB
Distinct19794
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Minimum2014-12-09 00:27:20+00:00
Maximum2015-10-15 10:48:48.026000+00:00
2023-07-24T21:27:11.385949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:11.498914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

international_delivery_mode
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
none
20000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters80000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 20000
100.0%

Length

2023-07-24T21:27:11.590944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:11.669488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
none 20000
100.0%

Most occurring characters

ValueCountFrequency (%)
n 40000
50.0%
o 20000
25.0%
e 20000
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40000
50.0%
o 20000
25.0%
e 20000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40000
50.0%
o 20000
25.0%
e 20000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 40000
50.0%
o 20000
25.0%
e 20000
25.0%

pictures
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

id
Text

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:11.815190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters240000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20000 ?
Unique (%)100.0%

Sample

1st rowMLA575840042
2nd rowMLA581925448
3rd rowMLA576675596
4th rowMLA577839604
5th rowMLA582753548
ValueCountFrequency (%)
mla575840042 1
 
< 0.1%
mla578573133 1
 
< 0.1%
mla582753548 1
 
< 0.1%
mla584537129 1
 
< 0.1%
mla580601135 1
 
< 0.1%
mla578401703 1
 
< 0.1%
mla584618060 1
 
< 0.1%
mla581284314 1
 
< 0.1%
mla581681737 1
 
< 0.1%
mla581744800 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
2023-07-24T21:27:12.065527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 33913
14.1%
8 23905
10.0%
7 23693
9.9%
M 20000
8.3%
L 20000
8.3%
A 20000
8.3%
3 14405
 
6.0%
2 14199
 
5.9%
4 14128
 
5.9%
9 14027
 
5.8%
Other values (3) 41730
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180000
75.0%
Uppercase Letter 60000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 33913
18.8%
8 23905
13.3%
7 23693
13.2%
3 14405
8.0%
2 14199
7.9%
4 14128
7.8%
9 14027
7.8%
0 13991
7.8%
1 13948
7.7%
6 13791
7.7%
Uppercase Letter
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 180000
75.0%
Latin 60000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 33913
18.8%
8 23905
13.3%
7 23693
13.2%
3 14405
8.0%
2 14199
7.9%
4 14128
7.8%
9 14027
7.8%
0 13991
7.8%
1 13948
7.7%
6 13791
7.7%
Latin
ValueCountFrequency (%)
M 20000
33.3%
L 20000
33.3%
A 20000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 33913
14.1%
8 23905
10.0%
7 23693
9.9%
M 20000
8.3%
L 20000
8.3%
A 20000
8.3%
3 14405
 
6.0%
2 14199
 
5.9%
4 14128
 
5.9%
9 14027
 
5.8%
Other values (3) 41730
17.4%

official_store_id
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)50.0%
Missing19824
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean197.25568
Minimum1
Maximum444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:12.181586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.25
Q179.25
median197
Q3313
95-th percentile410
Maximum444
Range443
Interquartile range (IQR)233.75

Descriptive statistics

Standard deviation127.37583
Coefficient of variation (CV)0.64573974
Kurtosis-1.1993756
Mean197.25568
Median Absolute Deviation (MAD)116.5
Skewness0.091753378
Sum34717
Variance16224.603
MonotonicityNot monotonic
2023-07-24T21:27:12.286740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 12
 
0.1%
197 11
 
0.1%
325 7
 
< 0.1%
229 6
 
< 0.1%
273 6
 
< 0.1%
333 5
 
< 0.1%
256 5
 
< 0.1%
23 4
 
< 0.1%
67 4
 
< 0.1%
216 4
 
< 0.1%
Other values (78) 112
 
0.6%
(Missing) 19824
99.1%
ValueCountFrequency (%)
1 3
< 0.1%
9 2
< 0.1%
11 2
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
20 2
< 0.1%
23 4
< 0.1%
25 3
< 0.1%
30 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
444 2
< 0.1%
429 1
 
< 0.1%
426 1
 
< 0.1%
420 1
 
< 0.1%
414 1
 
< 0.1%
413 3
< 0.1%
409 1
 
< 0.1%
408 1
 
< 0.1%
401 1
 
< 0.1%
392 1
 
< 0.1%

differential_pricing
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing20000
Missing (%)100.0%
Memory size312.5 KiB

accepts_mercadopago
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size175.8 KiB
True
19543 
False
 
457
ValueCountFrequency (%)
True 19543
97.7%
False 457
 
2.3%
2023-07-24T21:27:12.387805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

original_price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)93.3%
Missing19970
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean2337.2667
Minimum120
Maximum13999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:12.466486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile321.95
Q1461.5
median979.5
Q31883
95-th percentile11510.9
Maximum13999
Range13879
Interquartile range (IQR)1421.5

Descriptive statistics

Standard deviation3578.6258
Coefficient of variation (CV)1.5311158
Kurtosis5.2274019
Mean2337.2667
Median Absolute Deviation (MAD)575.5
Skewness2.4630719
Sum70118
Variance12806563
MonotonicityNot monotonic
2023-07-24T21:27:12.551260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
399 2
 
< 0.1%
599 2
 
< 0.1%
999 1
 
< 0.1%
10610 1
 
< 0.1%
1349 1
 
< 0.1%
5499 1
 
< 0.1%
2930 1
 
< 0.1%
359 1
 
< 0.1%
13999 1
 
< 0.1%
299 1
 
< 0.1%
Other values (18) 18
 
0.1%
(Missing) 19970
99.9%
ValueCountFrequency (%)
120 1
< 0.1%
299 1
< 0.1%
350 1
< 0.1%
359 1
< 0.1%
399 2
< 0.1%
440 1
< 0.1%
449 1
< 0.1%
499 1
< 0.1%
599 2
< 0.1%
640 1
< 0.1%
ValueCountFrequency (%)
13999 1
< 0.1%
12248 1
< 0.1%
10610 1
< 0.1%
5499 1
< 0.1%
2985 1
< 0.1%
2930 1
< 0.1%
2699 1
< 0.1%
1900 1
< 0.1%
1832 1
< 0.1%
1550 1
< 0.1%

currency_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
ARS
19882 
USD
 
118

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARS
2nd rowARS
3rd rowARS
4th rowARS
5th rowARS

Common Values

ValueCountFrequency (%)
ARS 19882
99.4%
USD 118
 
0.6%

Length

2023-07-24T21:27:12.639195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:12.719231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ars 19882
99.4%
usd 118
 
0.6%

Most occurring characters

ValueCountFrequency (%)
S 20000
33.3%
A 19882
33.1%
R 19882
33.1%
U 118
 
0.2%
D 118
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 20000
33.3%
A 19882
33.1%
R 19882
33.1%
U 118
 
0.2%
D 118
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 60000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 20000
33.3%
A 19882
33.1%
R 19882
33.1%
U 118
 
0.2%
D 118
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 20000
33.3%
A 19882
33.1%
R 19882
33.1%
U 118
 
0.2%
D 118
 
0.2%
Distinct19728
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:12.880241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length70
Median length63
Mean length61.34175
Min length0

Characters and Unicode

Total characters1226835
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19671 ?
Unique (%)98.4%

Sample

1st rowhttp://mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-I.jpg
2nd rowhttp://mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-I.jpg
3rd rowhttp://mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-I.jpg
4th rowhttp://mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-I.jpg
5th rowhttp://mla-s1-p.mlstatic.com/15889-MLA20110139801_062014-I.jpg
ValueCountFrequency (%)
http://mla-s2-p.mlstatic.com/14358-mla20085525309_042014-i.jpg 11
 
0.1%
http://mla-s2-p.mlstatic.com/12089-mla20053974504_022014-i.jpg 9
 
< 0.1%
http://mla-s2-p.mlstatic.com/15142-mla20096777529_052014-i.jpg 9
 
< 0.1%
http://mla-s2-p.mlstatic.com/14376-mla20085525316_042014-i.jpg 7
 
< 0.1%
http://mla-s1-p.mlstatic.com/4446-mla3620067895_012013-i.jpg 6
 
< 0.1%
http://www.mercadolibre.com/jm/img?s=stc&v=i&f=proccesing_image_es.jpg 5
 
< 0.1%
http://mla-s1-p.mlstatic.com/22282-mla20227229761_012015-i.jpg 5
 
< 0.1%
http://mla-s1-p.mlstatic.com/560801-mla20409172627_092015-i.jpg 4
 
< 0.1%
http://mla-s2-p.mlstatic.com/13520-mla3368171066_112012-i.jpg 4
 
< 0.1%
http://mla-s2-p.mlstatic.com/4066-mla132534862_9898-i.jpg 4
 
< 0.1%
Other values (19717) 19776
99.7%
2023-07-24T21:27:13.163255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85750
 
7.0%
t 79350
 
6.5%
- 79340
 
6.5%
2 76844
 
6.3%
1 76157
 
6.2%
p 59604
 
4.9%
m 59530
 
4.9%
/ 59525
 
4.9%
. 59520
 
4.9%
c 39690
 
3.2%
Other values (35) 551525
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 456539
37.2%
Decimal Number 452752
36.9%
Other Punctuation 138900
 
11.3%
Uppercase Letter 79360
 
6.5%
Dash Punctuation 79340
 
6.5%
Connector Punctuation 19929
 
1.6%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 79350
17.4%
p 59604
13.1%
m 59530
13.0%
c 39690
8.7%
s 39685
8.7%
a 39680
8.7%
l 39675
8.7%
i 19855
 
4.3%
g 19855
 
4.3%
o 19850
 
4.3%
Other values (10) 39765
8.7%
Decimal Number
ValueCountFrequency (%)
0 85750
18.9%
2 76844
17.0%
1 76157
16.8%
5 37434
8.3%
4 35938
7.9%
3 33929
 
7.5%
9 27850
 
6.2%
8 26783
 
5.9%
6 26406
 
5.8%
7 25661
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
I 19840
25.0%
A 19835
25.0%
L 19835
25.0%
M 19835
25.0%
S 5
 
< 0.1%
T 5
 
< 0.1%
C 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 59525
42.9%
. 59520
42.9%
: 19840
 
14.3%
& 10
 
< 0.1%
? 5
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 79340
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19929
100.0%
Math Symbol
ValueCountFrequency (%)
= 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 690936
56.3%
Latin 535899
43.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 79350
14.8%
p 59604
11.1%
m 59530
11.1%
c 39690
 
7.4%
s 39685
 
7.4%
a 39680
 
7.4%
l 39675
 
7.4%
i 19855
 
3.7%
g 19855
 
3.7%
o 19850
 
3.7%
Other values (17) 119125
22.2%
Common
ValueCountFrequency (%)
0 85750
12.4%
- 79340
11.5%
2 76844
11.1%
1 76157
11.0%
/ 59525
8.6%
. 59520
8.6%
5 37434
 
5.4%
4 35938
 
5.2%
3 33929
 
4.9%
9 27850
 
4.0%
Other values (8) 118649
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1226835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85750
 
7.0%
t 79350
 
6.5%
- 79340
 
6.5%
2 76844
 
6.3%
1 76157
 
6.2%
p 59604
 
4.9%
m 59530
 
4.9%
/ 59525
 
4.9%
. 59520
 
4.9%
c 39690
 
3.2%
Other values (35) 551525
45.0%

title
Text

Distinct19901
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:13.413662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length100
Median length72
Mean length45.2699
Min length3

Characters and Unicode

Total characters905398
Distinct characters138
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19852 ?
Unique (%)99.3%

Sample

1st rowPulsera Rolo Plata 925 Largo 20cm Muy Fina!!! Imperdible !!!
2nd rowOrganizador Tapperware
3rd rowEl Satiricon - Petronio - Envio Gratis Capital Federal -
4th rowPio Ix. Biografia. En Fasciculo. Envio Gratis.
5th rowCorsa 480 Vinuesa Rullo Chiarini Vignoles Ford Gt Mg Club Ar
ValueCountFrequency (%)
6840
 
4.6%
de 6518
 
4.4%
y 1743
 
1.2%
la 1402
 
1.0%
para 1211
 
0.8%
en 1120
 
0.8%
el 1068
 
0.7%
con 1034
 
0.7%
x 677
 
0.5%
a 664
 
0.5%
Other values (34495) 125173
84.9%
2023-07-24T21:27:13.776397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130578
 
14.4%
a 78850
 
8.7%
e 65125
 
7.2%
o 60628
 
6.7%
r 48556
 
5.4%
i 47780
 
5.3%
n 38573
 
4.3%
l 34957
 
3.9%
s 34286
 
3.8%
t 32502
 
3.6%
Other values (128) 333563
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 582081
64.3%
Space Separator 130596
 
14.4%
Uppercase Letter 126927
 
14.0%
Decimal Number 40822
 
4.5%
Other Punctuation 13721
 
1.5%
Dash Punctuation 7765
 
0.9%
Open Punctuation 1114
 
0.1%
Close Punctuation 1051
 
0.1%
Math Symbol 690
 
0.1%
Other Letter 202
 
< 0.1%
Other values (8) 429
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 78850
13.5%
e 65125
11.2%
o 60628
10.4%
r 48556
8.3%
i 47780
 
8.2%
n 38573
 
6.6%
l 34957
 
6.0%
s 34286
 
5.9%
t 32502
 
5.6%
u 20415
 
3.5%
Other values (37) 120409
20.7%
Uppercase Letter
ValueCountFrequency (%)
C 13822
 
10.9%
D 12262
 
9.7%
P 10414
 
8.2%
A 8756
 
6.9%
M 8669
 
6.8%
S 7557
 
6.0%
E 7554
 
6.0%
L 7198
 
5.7%
T 5822
 
4.6%
B 5749
 
4.5%
Other values (26) 39124
30.8%
Other Punctuation
ValueCountFrequency (%)
. 4994
36.4%
! 2481
18.1%
, 2347
17.1%
/ 2322
16.9%
: 364
 
2.7%
* 287
 
2.1%
# 282
 
2.1%
' 197
 
1.4%
& 185
 
1.3%
% 80
 
0.6%
Other values (7) 182
 
1.3%
Decimal Number
ValueCountFrequency (%)
0 8338
20.4%
1 7328
18.0%
2 5469
13.4%
5 3862
9.5%
3 3445
8.4%
4 2938
 
7.2%
9 2753
 
6.7%
6 2546
 
6.2%
8 2272
 
5.6%
7 1871
 
4.6%
Math Symbol
ValueCountFrequency (%)
+ 629
91.2%
| 35
 
5.1%
= 19
 
2.8%
~ 5
 
0.7%
¬ 2
 
0.3%
Other Symbol
ValueCountFrequency (%)
° 127
88.8%
® 15
 
10.5%
© 1
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
´ 64
68.8%
` 17
 
18.3%
¨ 12
 
12.9%
Space Separator
ValueCountFrequency (%)
130578
> 99.9%
  18
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1103
99.0%
[ 11
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 1040
99.0%
] 11
 
1.0%
Other Letter
ValueCountFrequency (%)
º 186
92.1%
ª 16
 
7.9%
Currency Symbol
ValueCountFrequency (%)
$ 119
99.2%
£ 1
 
0.8%
Other Number
ValueCountFrequency (%)
² 1
50.0%
¼ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 7765
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 65
100.0%
Final Punctuation
ValueCountFrequency (%)
» 4
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 709210
78.3%
Common 196188
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 78850
 
11.1%
e 65125
 
9.2%
o 60628
 
8.5%
r 48556
 
6.8%
i 47780
 
6.7%
n 38573
 
5.4%
l 34957
 
4.9%
s 34286
 
4.8%
t 32502
 
4.6%
u 20415
 
2.9%
Other values (75) 247538
34.9%
Common
ValueCountFrequency (%)
130578
66.6%
0 8338
 
4.3%
- 7765
 
4.0%
1 7328
 
3.7%
2 5469
 
2.8%
. 4994
 
2.5%
5 3862
 
2.0%
3 3445
 
1.8%
4 2938
 
1.5%
9 2753
 
1.4%
Other values (43) 18718
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900524
99.5%
None 4874
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130578
 
14.5%
a 78850
 
8.8%
e 65125
 
7.2%
o 60628
 
6.7%
r 48556
 
5.4%
i 47780
 
5.3%
n 38573
 
4.3%
l 34957
 
3.9%
s 34286
 
3.8%
t 32502
 
3.6%
Other values (79) 328689
36.5%
None
ValueCountFrequency (%)
ñ 1360
27.9%
ó 915
18.8%
í 714
14.6%
á 569
11.7%
é 435
 
8.9%
º 186
 
3.8%
ú 136
 
2.8%
° 127
 
2.6%
´ 64
 
1.3%
¡ 50
 
1.0%
Other values (39) 318
 
6.5%

automatic_relist
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size175.8 KiB
False
19086 
True
 
914
ValueCountFrequency (%)
False 19086
95.4%
True 914
 
4.6%
2023-07-24T21:27:13.883682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct19386
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Minimum2014-03-13 21:07:44+00:00
Maximum2015-10-15 08:55:05+00:00
2023-07-24T21:27:13.973715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:14.110135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct19728
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:14.322952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length82
Median length81
Mean length80.18525
Min length0

Characters and Unicode

Total characters1603705
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19671 ?
Unique (%)98.4%

Sample

1st rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-I.jpg
2nd rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-I.jpg
3rd rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-I.jpg
4th rowhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-I.jpg
5th rowhttps://a248.e.akamai.net/mla-s1-p.mlstatic.com/15889-MLA20110139801_062014-I.jpg
ValueCountFrequency (%)
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/14358-mla20085525309_042014-i.jpg 11
 
0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/12089-mla20053974504_022014-i.jpg 9
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/15142-mla20096777529_052014-i.jpg 9
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/14376-mla20085525316_042014-i.jpg 7
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/4446-mla3620067895_012013-i.jpg 6
 
< 0.1%
https://www.mercadolibre.com/jm/img?s=stc&v=i&f=proccesing_image_es.jpg 5
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/22282-mla20227229761_012015-i.jpg 5
 
< 0.1%
https://a248.e.akamai.net/mla-s1-p.mlstatic.com/560801-mla20409172627_092015-i.jpg 4
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/13520-mla3368171066_112012-i.jpg 4
 
< 0.1%
https://a248.e.akamai.net/mla-s2-p.mlstatic.com/4066-mla132534862_9898-i.jpg 4
 
< 0.1%
Other values (19717) 19776
99.7%
2023-07-24T21:27:14.676413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 119025
 
7.4%
a 119020
 
7.4%
t 99185
 
6.2%
2 96679
 
6.0%
0 85750
 
5.3%
m 79365
 
4.9%
/ 79360
 
4.9%
- 79340
 
4.9%
1 76157
 
4.7%
p 59604
 
3.7%
Other values (36) 710220
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 694564
43.3%
Decimal Number 512257
31.9%
Other Punctuation 218240
 
13.6%
Uppercase Letter 79360
 
4.9%
Dash Punctuation 79340
 
4.9%
Connector Punctuation 19929
 
1.2%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 119020
17.1%
t 99185
14.3%
m 79365
11.4%
p 59604
8.6%
s 59525
8.6%
e 39695
 
5.7%
i 39690
 
5.7%
c 39690
 
5.7%
l 39675
 
5.7%
g 19855
 
2.9%
Other values (11) 99260
14.3%
Decimal Number
ValueCountFrequency (%)
2 96679
18.9%
0 85750
16.7%
1 76157
14.9%
4 55773
10.9%
8 46618
9.1%
5 37434
 
7.3%
3 33929
 
6.6%
9 27850
 
5.4%
6 26406
 
5.2%
7 25661
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
I 19840
25.0%
M 19835
25.0%
A 19835
25.0%
L 19835
25.0%
S 5
 
< 0.1%
T 5
 
< 0.1%
C 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 119025
54.5%
/ 79360
36.4%
: 19840
 
9.1%
& 10
 
< 0.1%
? 5
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 79340
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19929
100.0%
Math Symbol
ValueCountFrequency (%)
= 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 829781
51.7%
Latin 773924
48.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 119020
15.4%
t 99185
12.8%
m 79365
10.3%
p 59604
 
7.7%
s 59525
 
7.7%
e 39695
 
5.1%
i 39690
 
5.1%
c 39690
 
5.1%
l 39675
 
5.1%
g 19855
 
2.6%
Other values (18) 178620
23.1%
Common
ValueCountFrequency (%)
. 119025
14.3%
2 96679
11.7%
0 85750
10.3%
/ 79360
9.6%
- 79340
9.6%
1 76157
9.2%
4 55773
6.7%
8 46618
 
5.6%
5 37434
 
4.5%
3 33929
 
4.1%
Other values (8) 119716
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1603705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 119025
 
7.4%
a 119020
 
7.4%
t 99185
 
6.2%
2 96679
 
6.0%
0 85750
 
5.3%
m 79365
 
4.9%
/ 79360
 
4.9%
- 79340
 
4.9%
1 76157
 
4.7%
p 59604
 
3.7%
Other values (36) 710220
44.3%
Distinct19305
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Minimum2015-10-15 03:21:01+00:00
Maximum2024-11-04 14:55:51+00:00
2023-07-24T21:27:15.680760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:15.796777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
active
19126 
paused
 
872
closed
 
2

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters120000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowactive
2nd rowactive
3rd rowactive
4th rowactive
5th rowactive

Common Values

ValueCountFrequency (%)
active 19126
95.6%
paused 872
 
4.4%
closed 2
 
< 0.1%

Length

2023-07-24T21:27:15.904525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:15.998577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
active 19126
95.6%
paused 872
 
4.4%
closed 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 20000
16.7%
a 19998
16.7%
c 19128
15.9%
t 19126
15.9%
i 19126
15.9%
v 19126
15.9%
s 874
 
0.7%
d 874
 
0.7%
p 872
 
0.7%
u 872
 
0.7%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120000
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20000
16.7%
a 19998
16.7%
c 19128
15.9%
t 19126
15.9%
i 19126
15.9%
v 19126
15.9%
s 874
 
0.7%
d 874
 
0.7%
p 872
 
0.7%
u 872
 
0.7%
Other values (2) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 120000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20000
16.7%
a 19998
16.7%
c 19128
15.9%
t 19126
15.9%
i 19126
15.9%
v 19126
15.9%
s 874
 
0.7%
d 874
 
0.7%
p 872
 
0.7%
u 872
 
0.7%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20000
16.7%
a 19998
16.7%
c 19128
15.9%
t 19126
15.9%
i 19126
15.9%
v 19126
15.9%
s 874
 
0.7%
d 874
 
0.7%
p 872
 
0.7%
u 872
 
0.7%
Other values (2) 4
 
< 0.1%

video_id
Text

MISSING 

Distinct465
Distinct (%)78.4%
Missing19407
Missing (%)97.0%
Memory size312.5 KiB
2023-07-24T21:27:16.205735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters6523
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique438 ?
Unique (%)73.9%

Sample

1st rowwXNX7YhAAP8
2nd rowTcbGrvPAf6Q
3rd rowLQf4gS3ooRk
4th rownhbFbFdY-pY
5th row7iK1AAoJ-Sc
ValueCountFrequency (%)
qqnfoice_o8 56
 
9.4%
evcquwl7rie 21
 
3.5%
t0xbm8fb1eg 9
 
1.5%
u7okrylubno 6
 
1.0%
6jhmxwttjoa 5
 
0.8%
mynrc5ia1sk 5
 
0.8%
orxwqovjvxg 4
 
0.7%
fyejix4ufak 3
 
0.5%
ljoe9junap4 3
 
0.5%
zcrmpjetdie 3
 
0.5%
Other values (455) 478
80.6%
2023-07-24T21:27:16.533425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Q 242
 
3.7%
E 219
 
3.4%
o 191
 
2.9%
8 172
 
2.6%
c 157
 
2.4%
i 146
 
2.2%
N 139
 
2.1%
I 134
 
2.1%
O 131
 
2.0%
_ 128
 
2.0%
Other values (54) 4864
74.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2675
41.0%
Lowercase Letter 2674
41.0%
Decimal Number 971
 
14.9%
Connector Punctuation 128
 
2.0%
Dash Punctuation 75
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 242
 
9.0%
E 219
 
8.2%
N 139
 
5.2%
I 134
 
5.0%
O 131
 
4.9%
Y 119
 
4.4%
U 115
 
4.3%
R 112
 
4.2%
M 110
 
4.1%
C 103
 
3.9%
Other values (16) 1251
46.8%
Lowercase Letter
ValueCountFrequency (%)
o 191
 
7.1%
c 157
 
5.9%
i 146
 
5.5%
g 127
 
4.7%
w 127
 
4.7%
f 122
 
4.6%
k 121
 
4.5%
u 120
 
4.5%
s 103
 
3.9%
t 100
 
3.7%
Other values (16) 1360
50.9%
Decimal Number
ValueCountFrequency (%)
8 172
17.7%
4 124
12.8%
0 111
11.4%
1 91
9.4%
7 87
9.0%
6 85
8.8%
5 85
8.8%
2 81
8.3%
9 69
7.1%
3 66
 
6.8%
Connector Punctuation
ValueCountFrequency (%)
_ 128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5349
82.0%
Common 1174
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 242
 
4.5%
E 219
 
4.1%
o 191
 
3.6%
c 157
 
2.9%
i 146
 
2.7%
N 139
 
2.6%
I 134
 
2.5%
O 131
 
2.4%
g 127
 
2.4%
w 127
 
2.4%
Other values (42) 3736
69.8%
Common
ValueCountFrequency (%)
8 172
14.7%
_ 128
10.9%
4 124
10.6%
0 111
9.5%
1 91
7.8%
7 87
7.4%
6 85
7.2%
5 85
7.2%
2 81
6.9%
- 75
6.4%
Other values (2) 135
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q 242
 
3.7%
E 219
 
3.4%
o 191
 
2.9%
8 172
 
2.6%
c 157
 
2.4%
i 146
 
2.2%
N 139
 
2.1%
I 134
 
2.1%
O 131
 
2.0%
_ 128
 
2.0%
Other values (54) 4864
74.6%

catalog_product_id
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct2
Distinct (%)100.0%
Missing19998
Missing (%)> 99.9%
Memory size312.5 KiB
5093232.0
3051112.0

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters18
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row5093232.0
2nd row3051112.0

Common Values

ValueCountFrequency (%)
5093232.0 1
 
< 0.1%
3051112.0 1
 
< 0.1%
(Missing) 19998
> 99.9%

Length

2023-07-24T21:27:16.642775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:27:16.731607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5093232.0 1
50.0%
3051112.0 1
50.0%

Most occurring characters

ValueCountFrequency (%)
0 4
22.2%
3 3
16.7%
2 3
16.7%
1 3
16.7%
5 2
11.1%
. 2
11.1%
9 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
88.9%
Other Punctuation 2
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
25.0%
3 3
18.8%
2 3
18.8%
1 3
18.8%
5 2
12.5%
9 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
22.2%
3 3
16.7%
2 3
16.7%
1 3
16.7%
5 2
11.1%
. 2
11.1%
9 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
22.2%
3 3
16.7%
2 3
16.7%
1 3
16.7%
5 2
11.1%
. 2
11.1%
9 1
 
5.6%

subtitle
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing20000
Missing (%)100.0%
Memory size312.5 KiB

initial_quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct221
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.29915
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:16.832574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile47
Maximum9999
Range9998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation411.80625
Coefficient of variation (CV)12.006311
Kurtosis530.81818
Mean34.29915
Median Absolute Deviation (MAD)0
Skewness22.387782
Sum685983
Variance169584.39
MonotonicityNot monotonic
2023-07-24T21:27:16.950729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14013
70.1%
2 1007
 
5.0%
10 921
 
4.6%
3 701
 
3.5%
5 515
 
2.6%
4 393
 
2.0%
100 221
 
1.1%
20 221
 
1.1%
6 200
 
1.0%
9 145
 
0.7%
Other values (211) 1663
 
8.3%
ValueCountFrequency (%)
1 14013
70.1%
2 1007
 
5.0%
3 701
 
3.5%
4 393
 
2.0%
5 515
 
2.6%
6 200
 
1.0%
7 111
 
0.6%
8 138
 
0.7%
9 145
 
0.7%
10 921
 
4.6%
ValueCountFrequency (%)
9999 20
0.1%
9998 3
 
< 0.1%
9996 1
 
< 0.1%
9991 1
 
< 0.1%
9984 1
 
< 0.1%
9977 1
 
< 0.1%
9932 1
 
< 0.1%
9879 1
 
< 0.1%
9828 1
 
< 0.1%
9789 1
 
< 0.1%
Distinct19355
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
Minimum2014-03-13 21:07:44+00:00
Maximum2015-10-15 08:55:05+00:00
2023-07-24T21:27:17.058783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:17.175109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

permalink
Text

UNIQUE 

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:17.406533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length146
Median length116
Mean length97.5835
Min length57

Characters and Unicode

Total characters1951670
Distinct characters45
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20000 ?
Unique (%)100.0%

Sample

1st rowhttp://articulo.mercadolibre.com.ar/MLA-575840042-pulsera-rolo-plata-925-largo-20cm-muy-fina-imperdible--_JM
2nd rowhttp://articulo.mercadolibre.com.ar/MLA-581925448-organizador-tapperware-_JM
3rd rowhttp://articulo.mercadolibre.com.ar/MLA-576675596-el-satiricon-petronio-envio-gratis-capital-federal--_JM
4th rowhttp://articulo.mercadolibre.com.ar/MLA-577839604-pio-ix-biografia-en-fasciculo-envio-gratis-_JM
5th rowhttp://articulo.mercadolibre.com.ar/MLA-582753548-corsa-480-vinuesa-rullo-chiarini-vignoles-ford-gt-mg-club-ar-_JM
ValueCountFrequency (%)
http://articulo.mercadolibre.com.ar/mla-575840042-pulsera-rolo-plata-925-largo-20cm-muy-fina-imperdible--_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-578573133-conjunto-2-piezas-carters-remera-y-pantalon-importado-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-582753548-corsa-480-vinuesa-rullo-chiarini-vignoles-ford-gt-mg-club-ar-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-584537129-tc-helicon-harmony-singer-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-580601135-kit-2-amortiguadores-traseros-sachs-toyota-rav-4-94-8328g-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-578401703-el-ultimo-judio-noah-gordon-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-584618060-publicidad-mitsubishi-montero-nativa-l200-ano-1998-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-581284314-pet-shop-muneca-con-animalitos-zap-a4957-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-581681737-candy-bar-kit-imprimible-bubble-guppies-cumple-y-mas-_jm 1
 
< 0.1%
http://articulo.mercadolibre.com.ar/mla-581744800-manual-de-his-economica-regimen-juridico-recursos-natu-_jm 1
 
< 0.1%
Other values (19990) 19990
> 99.9%
2023-07-24T21:27:17.753812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 182960
 
9.4%
a 148177
 
7.6%
r 133189
 
6.8%
o 124250
 
6.4%
e 113421
 
5.8%
t 98270
 
5.0%
c 93781
 
4.8%
i 91295
 
4.7%
l 81757
 
4.2%
m 63196
 
3.2%
Other values (35) 821374
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1287888
66.0%
Decimal Number 220822
 
11.3%
Dash Punctuation 182960
 
9.4%
Other Punctuation 140000
 
7.2%
Uppercase Letter 100000
 
5.1%
Connector Punctuation 20000
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 148177
11.5%
r 133189
10.3%
o 124250
9.6%
e 113421
 
8.8%
t 98270
 
7.6%
c 93781
 
7.3%
i 91295
 
7.1%
l 81757
 
6.3%
m 63196
 
4.9%
d 51197
 
4.0%
Other values (16) 289355
22.5%
Decimal Number
ValueCountFrequency (%)
5 37775
17.1%
8 26177
11.9%
7 25564
11.6%
0 22329
10.1%
1 21276
9.6%
2 19668
8.9%
3 17850
8.1%
4 17066
7.7%
9 16780
7.6%
6 16337
7.4%
Uppercase Letter
ValueCountFrequency (%)
M 40000
40.0%
A 20000
20.0%
J 20000
20.0%
L 20000
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 60000
42.9%
. 60000
42.9%
: 20000
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 182960
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1387888
71.1%
Common 563782
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 148177
 
10.7%
r 133189
 
9.6%
o 124250
 
9.0%
e 113421
 
8.2%
t 98270
 
7.1%
c 93781
 
6.8%
i 91295
 
6.6%
l 81757
 
5.9%
m 63196
 
4.6%
d 51197
 
3.7%
Other values (20) 389355
28.1%
Common
ValueCountFrequency (%)
- 182960
32.5%
/ 60000
 
10.6%
. 60000
 
10.6%
5 37775
 
6.7%
8 26177
 
4.6%
7 25564
 
4.5%
0 22329
 
4.0%
1 21276
 
3.8%
: 20000
 
3.5%
_ 20000
 
3.5%
Other values (5) 87701
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1951670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 182960
 
9.4%
a 148177
 
7.6%
r 133189
 
6.8%
o 124250
 
6.4%
e 113421
 
5.8%
t 98270
 
5.0%
c 93781
 
4.8%
i 91295
 
4.7%
l 81757
 
4.2%
m 63196
 
3.2%
Other values (35) 821374
42.1%

geolocation
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size312.5 KiB

sold_quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct148
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7943
Minimum0
Maximum8676
Zeros16618
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:17.865396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum8676
Range8676
Interquartile range (IQR)0

Descriptive statistics

Standard deviation76.914826
Coefficient of variation (CV)27.525615
Kurtosis10016.146
Mean2.7943
Median Absolute Deviation (MAD)0
Skewness96.450576
Sum55886
Variance5915.8905
MonotonicityNot monotonic
2023-07-24T21:27:17.968908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16618
83.1%
1 1277
 
6.4%
2 520
 
2.6%
3 246
 
1.2%
4 197
 
1.0%
5 141
 
0.7%
6 106
 
0.5%
7 78
 
0.4%
10 68
 
0.3%
8 63
 
0.3%
Other values (138) 686
 
3.4%
ValueCountFrequency (%)
0 16618
83.1%
1 1277
 
6.4%
2 520
 
2.6%
3 246
 
1.2%
4 197
 
1.0%
5 141
 
0.7%
6 106
 
0.5%
7 78
 
0.4%
8 63
 
0.3%
9 57
 
0.3%
ValueCountFrequency (%)
8676 1
< 0.1%
6065 1
< 0.1%
692 1
< 0.1%
689 1
< 0.1%
637 1
< 0.1%
575 1
< 0.1%
525 1
< 0.1%
498 1
< 0.1%
494 1
< 0.1%
459 1
< 0.1%

available_quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct234
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.04755
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.5 KiB
2023-07-24T21:27:18.074795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile43
Maximum9999
Range9998
Interquartile range (IQR)1

Descriptive statistics

Standard deviation411.54107
Coefficient of variation (CV)12.087245
Kurtosis531.57872
Mean34.04755
Median Absolute Deviation (MAD)0
Skewness22.409085
Sum680951
Variance169366.05
MonotonicityNot monotonic
2023-07-24T21:27:18.183408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14128
70.6%
2 1030
 
5.1%
10 864
 
4.3%
3 749
 
3.7%
5 465
 
2.3%
4 400
 
2.0%
100 204
 
1.0%
20 192
 
1.0%
6 181
 
0.9%
9 163
 
0.8%
Other values (224) 1624
 
8.1%
ValueCountFrequency (%)
1 14128
70.6%
2 1030
 
5.1%
3 749
 
3.7%
4 400
 
2.0%
5 465
 
2.3%
6 181
 
0.9%
7 114
 
0.6%
8 135
 
0.7%
9 163
 
0.8%
10 864
 
4.3%
ValueCountFrequency (%)
9999 10
0.1%
9998 5
< 0.1%
9996 5
< 0.1%
9994 1
 
< 0.1%
9991 1
 
< 0.1%
9989 1
 
< 0.1%
9984 1
 
< 0.1%
9977 1
 
< 0.1%
9976 1
 
< 0.1%
9966 1
 
< 0.1%

Interactions

2023-07-24T21:27:05.051737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.172383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.880494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.626498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.320045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.940679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.626550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.351666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.143467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.274373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.976494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.714533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.395123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.019500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.719562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.442666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.245034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.375373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.084493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.815567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.474162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.106501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.826879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.541666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.334058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.462373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.174527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.901575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.552130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.194536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.924592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.628665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.409204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.536592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.253494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.976619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.623129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.299540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.997937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.705667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.492721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.616156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.341494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.056605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.702163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.386521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.082594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.789734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.581279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.705531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.439494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.143643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.778140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.465519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.171636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.877735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:05.674663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:00.790540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:01.531536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.231034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:02.858766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:03.547544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.257635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-24T21:27:04.962735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-07-24T21:27:18.284375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
base_priceseller_idpriceofficial_store_idoriginal_priceinitial_quantitysold_quantityavailable_quantityconditionlisting_type_idbuying_modeaccepts_mercadopagocurrency_idautomatic_reliststatuscatalog_product_id
base_price1.0000.1191.0000.1030.9840.0620.0530.0600.0000.0140.0450.0210.0000.0000.0001.000
seller_id0.1191.0000.1190.498-0.2220.1030.0010.1030.1770.0860.0500.0520.0360.1650.0381.000
price1.0000.1191.0000.1030.9840.0620.0530.0600.0000.0140.0450.0210.0000.0000.0001.000
official_store_id0.1030.4980.1031.0000.1270.086-0.2110.1000.3350.0581.0001.0001.0000.7260.1840.000
original_price0.984-0.2220.9840.1271.0000.4500.5120.4171.0000.3601.0001.0001.0000.0000.0000.000
initial_quantity0.0620.1030.0620.0860.4501.0000.3400.9900.0370.0440.0000.0000.0000.0500.0001.000
sold_quantity0.0530.0010.053-0.2110.5120.3401.0000.3070.0000.0310.0000.0000.0000.0000.0001.000
available_quantity0.0600.1030.0600.1000.4170.9900.3071.0000.0360.0410.0000.0000.0000.0490.0001.000
condition0.0000.1770.0000.3351.0000.0370.0000.0361.0000.5150.1110.1030.0270.1800.0581.000
listing_type_id0.0140.0860.0140.0580.3600.0440.0310.0410.5151.0000.2910.4110.2210.8040.0451.000
buying_mode0.0450.0500.0451.0001.0000.0000.0000.0000.1110.2911.0001.0000.5040.0380.0411.000
accepts_mercadopago0.0210.0520.0211.0001.0000.0000.0000.0000.1030.4111.0001.0000.5020.0320.0111.000
currency_id0.0000.0360.0001.0001.0000.0000.0000.0000.0270.2210.5040.5021.0000.0140.0091.000
automatic_relist0.0000.1650.0000.7260.0000.0500.0000.0490.1800.8040.0380.0320.0141.0000.0381.000
status0.0000.0380.0000.1840.0000.0000.0000.0000.0580.0450.0410.0110.0090.0381.0001.000
catalog_product_id1.0001.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-07-24T21:27:06.188618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-24T21:27:07.387650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-24T21:27:07.989296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seller_addresswarrantysub_statusconditionseller_contactdeal_idsbase_priceshippingnon_mercado_pago_payment_methodsseller_idvariationslocationsite_idlisting_type_idpriceattributesbuying_modetagslisting_sourceparent_item_idcoverage_areascategory_iddescriptionslast_updatedinternational_delivery_modepicturesidofficial_store_iddifferential_pricingaccepts_mercadopagooriginal_pricecurrency_idthumbnailtitleautomatic_relistdate_createdsecure_thumbnailstop_timestatusvideo_idcatalog_product_idsubtitleinitial_quantitystart_timepermalinkgeolocationsold_quantityavailable_quantity
34460{'comment': '', 'longitude': -58.4425256, 'id': 132099068, 'country': {'name': 'Argentina', 'id': 'AR'}, 'address_line': '', 'latitude': -34.6312518, 'search_location': {'neighborhood': {'name': '', 'id': ''}, 'state': {'name': 'Capital Federal', 'id': 'TUxBUENBUGw3M2E1'}, 'city': {'name': 'Capital Federal', 'id': 'TUxBQ0NBUGZlZG1sYQ'}}, 'zip_code': '', 'city': {'name': 'Capital Federal', 'id': ''}, 'state': {'name': 'Capital Federal', 'id': 'AR-C'}}None[]newNone[]449.90{'local_pick_up': True, 'free_methods': [{'rule': {'value': None, 'free_mode': 'country'}, 'id': 73328}], 'tags': [], 'free_shipping': True, 'mode': 'me2', 'dimensions': None}[{'description': 'Efectivo', 'id': 'MLAMO', 'type': 'G'}]147134523[]{}MLAgold449.90[]buy_it_now[dragged_bids_and_visits]MLA566029044[]MLA6446[{'id': 'MLA575840042-901670705'}]2015-10-08T12:43:49.000Znone[{'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-O.jpg', 'quality': '', 'id': '355901-MLA20435710456_092015'}, {'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/370901-MLA20435712267_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s2-p.mlstatic.com/370901-MLA20435712267_092015-O.jpg', 'quality': '', 'id': '370901-MLA20435712267_092015'}, {'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/322901-MLA20435714261_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s2-p.mlstatic.com/322901-MLA20435714261_092015-O.jpg', 'quality': '', 'id': '322901-MLA20435714261_092015'}, {'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s1-p.mlstatic.com/520901-MLA20435716055_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s1-p.mlstatic.com/520901-MLA20435716055_092015-O.jpg', 'quality': '', 'id': '520901-MLA20435716055_092015'}, {'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/560901-MLA20435715648_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s2-p.mlstatic.com/560901-MLA20435715648_092015-O.jpg', 'quality': '', 'id': '560901-MLA20435715648_092015'}, {'size': '500x500', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/702901-MLA20435717107_092015-O.jpg', 'max_size': '1200x1200', 'url': 'http://mla-s2-p.mlstatic.com/702901-MLA20435717107_092015-O.jpg', 'quality': '', 'id': '702901-MLA20435717107_092015'}]MLA575840042NaNNoneTrueNaNARShttp://mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-I.jpgPulsera Rolo Plata 925 Largo 20cm Muy Fina!!! Imperdible !!!False2015-08-23T11:46:41.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/355901-MLA20435710456_092015-I.jpg2015-10-22T11:46:41.000ZactiveNoneNaNNone32015-08-23T11:46:41.000Zhttp://articulo.mercadolibre.com.ar/MLA-575840042-pulsera-rolo-plata-925-largo-20cm-muy-fina-imperdible--_JM{'latitude': -34.6312518, 'longitude': -58.4425256}201
83465{'comment': '', 'longitude': -58.3878096, 'id': 131779779, 'country': {'name': 'Argentina', 'id': 'AR'}, 'address_line': '', 'latitude': -34.7562565, 'search_location': {'neighborhood': {'name': '', 'id': ''}, 'state': {'name': 'Bs.As. G.B.A. Sur', 'id': 'TUxBUEdSQXJlMDNm'}, 'city': {'name': 'Lomas de Zamora', 'id': 'TUxBQ0xPTWMwNjk3'}}, 'zip_code': '', 'city': {'name': 'lomas de zamora', 'id': ''}, 'state': {'name': 'Buenos Aires', 'id': 'AR-B'}}None[]newNone[]170.00{'local_pick_up': False, 'methods': [], 'tags': [], 'free_shipping': False, 'mode': 'not_specified', 'dimensions': None}[]153804577[]{}MLAbronze170.00[]buy_it_now[dragged_bids_and_visits]MLA564993666[]MLA3530[{'id': 'MLA581925448-932513701'}]2015-09-28T22:37:02.000Znone[{'size': '500x375', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-O.jpg', 'max_size': '1200x900', 'url': 'http://mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-O.jpg', 'quality': '', 'id': '11501-MLA20046304125_022014'}, {'size': '500x375', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/11543-MLA20046303405_022014-O.jpg', 'max_size': '1200x900', 'url': 'http://mla-s2-p.mlstatic.com/11543-MLA20046303405_022014-O.jpg', 'quality': '', 'id': '11543-MLA20046303405_022014'}, {'size': '500x375', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/11545-MLA20046303421_022014-O.jpg', 'max_size': '1200x900', 'url': 'http://mla-s2-p.mlstatic.com/11545-MLA20046303421_022014-O.jpg', 'quality': '', 'id': '11545-MLA20046303421_022014'}]MLA581925448NaNNoneTrueNaNARShttp://mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-I.jpgOrganizador TapperwareFalse2015-09-28T22:36:57.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/11501-MLA20046304125_022014-I.jpg2015-11-27T22:36:57.000ZactiveNoneNaNNone12015-09-28T22:36:57.000Zhttp://articulo.mercadolibre.com.ar/MLA-581925448-organizador-tapperware-_JM{'latitude': -34.7562565, 'longitude': -58.3878096}01
89446{'comment': '', 'longitude': -58.4712517, 'id': 151411099, 'country': {'name': 'Argentina', 'id': 'AR'}, 'address_line': '', 'latitude': -34.6214881, 'search_location': {'neighborhood': {'name': 'Flores', 'id': 'TUxBQkZMTzMwNzRa'}, 'state': {'name': 'Capital Federal', 'id': 'TUxBUENBUGw3M2E1'}, 'city': {'name': 'Capital Federal', 'id': 'TUxBQ0NBUGZlZG1sYQ'}}, 'zip_code': '', 'city': {'name': 'Flores', 'id': 'TUxBQkZMTzMwNzRa'}, 'state': {'name': 'Capital Federal', 'id': 'AR-C'}}Con garantia.[]newNone[]70.00{'local_pick_up': True, 'methods': [], 'tags': [], 'free_shipping': False, 'mode': 'me2', 'dimensions': None}[{'description': 'Transferencia bancaria', 'id': 'MLATB', 'type': 'G'}, {'description': 'Tarjeta de crédito', 'id': 'MLAOT', 'type': 'N'}, {'description': 'Efectivo', 'id': 'MLAMO', 'type': 'G'}]44217240[]{}MLAbronze70.00[]buy_it_now[dragged_bids_and_visits]MLA568624909[]MLA41287[{'id': 'MLA576675596-905654184'}]2015-10-05T03:24:35.000Znone[{'size': '500x281', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-O.jpg', 'max_size': '1200x675', 'url': 'http://mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-O.jpg', 'quality': '', 'id': '22412-MLA20231035311_012015'}]MLA576675596NaNNoneTrueNaNARShttp://mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-I.jpgEl Satiricon - Petronio - Envio Gratis Capital Federal -False2015-08-28T01:06:27.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/22412-MLA20231035311_012015-I.jpg2015-10-27T01:06:27.000ZactiveNoneNaNNone22015-08-28T01:06:27.000Zhttp://articulo.mercadolibre.com.ar/MLA-576675596-el-satiricon-petronio-envio-gratis-capital-federal--_JM{'latitude': -34.6214881, 'longitude': -58.4712517}22
62011{'comment': '', 'longitude': -58.5466251, 'id': 152917459, 'country': {'name': 'Argentina', 'id': 'AR'}, 'address_line': '', 'latitude': -34.5147867, 'search_location': {'neighborhood': {'name': 'Villa Adelina', 'id': 'TUxBQlZJTDI2MDZa'}, 'state': {'name': 'Bs.As. G.B.A. Norte', 'id': 'TUxBUEdSQWU4ZDkz'}, 'city': {'name': 'San Isidro', 'id': 'TUxBQ1NBTjg4ZmJk'}}, 'zip_code': '', 'city': {'name': 'Villa Adelina', 'id': ''}, 'state': {'name': 'Buenos Aires', 'id': 'AR-B'}}[]usedNone[]39.89{'local_pick_up': True, 'methods': [], 'tags': [], 'free_shipping': False, 'mode': 'me2', 'dimensions': None}[{'description': 'Acordar con el comprador', 'id': 'MLAWC', 'type': 'G'}, {'description': 'Efectivo', 'id': 'MLAMO', 'type': 'G'}]85584503[]{}MLAbronze39.89[]buy_it_now[dragged_bids_and_visits]MLA567996849[]MLA41174[{'id': 'MLA577839604-911725409'}]2015-09-23T18:02:44.000Znone[{'size': '500x375', 'secure_url': 'https://a248.e.akamai.net/mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-O.jpg', 'max_size': '1200x900', 'url': 'http://mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-O.jpg', 'quality': '', 'id': '4966-MLA3967481689_032013'}]MLA577839604NaNNoneTrueNaNARShttp://mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-I.jpgPio Ix. Biografia. En Fasciculo. Envio Gratis.False2015-09-04T13:49:05.000Zhttps://a248.e.akamai.net/mla-s2-p.mlstatic.com/4966-MLA3967481689_032013-I.jpg2015-11-03T13:49:04.000ZactiveNoneNaNNone12015-09-04T13:49:04.000Zhttp://articulo.mercadolibre.com.ar/MLA-577839604-pio-ix-biografia-en-fasciculo-envio-gratis-_JM{'latitude': -34.5147867, 'longitude': -58.5466251}01
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